Africa and the Winter Olympics – two topics that aren’t often thought of together. But in Sochi this year, five athletes will be representing three African nations – Morocco, Togo and Zimbabwe. For Togo and Zimbabwe, it’s their Winter Olympic debut.

As Africa figures more prominently in the world economy, its nations are stepping onto all sorts of world stages – even some as surprising as the Winter Olympic games.

In 2014, the IBM Center for Applied Insights also had its own African debut – publishing, for the first time, research involving 180 IT leaders from six African nations, including Morocco, Egypt, South Africa, Kenya and Nigeria.

In our study, we identified a segment of African IT leaders – who like Africa’s Olympic medal hopefuls – are preparing to compete on a different plane by capitalizing on mobile, social, cloud and analytics technologies. We call them pacesetters. And they too are navigating tough courses – around challenges such as the need for greater strategic business leadership from IT leaders, IT skills development in emerging technologies, and effective information security as the use of mobile, social, cloud and analytics skyrockets.

As you watch Africa’s athletes compete at alpine and cross-country skiing this week, give some thought to the impact Africa’s businesses – and its growing middle-class – will have on our global economy.

What do you do when your insights challenge prevailing beliefs? That’s the question we faced at IBM in 2007 when a brand new forecasting tool we’d developed started spitting out projections that conflicted with our other forecasts.

Large companies, like IBM, can really struggle with pulling together complete and relevant data to create accurate forecasts. We initially developed the new forecasting tool when IBM missed an earnings target in 2005. After studying the forecasting technology we had been relying on, the Market Development and Insights team discovered that its focus on the longer term had failed to pick up on a shift in the market. That meant we had to improve the accuracy of the longer term view with some sort of early warning system to flag abrupt changes in market direction.

The new tool was designed to track over 2,000 market indicators in the G7 and BRIC countries, including oil prices, key manufacture goods and transportation, on a monthly basis. It uses correlation techniques, regression testing and principal components analysis to identify the best indicators, given the current market climate. Then it combines them to produce a forecast of the market for the next three quarters.

By 2007, our early warning system was ready to roll out to handle short term forecasting and supplement the long view.

There was just one issue: No one knew whether to trust it. Because it was forecasting that black clouds were gathering on the horizon. But our long view and all the third-party sources we used said the sky was blue.

IBM’s top management was understandably dubious of the predictions of an unproven new forecasting tool. So we decided to take the time to track economic data on our own very carefully for two quarters and compare it with forecasted findings.

The result? The alert tool was more accurate. In fact, it was so accurate that we were able to predict the recent global recession two quarters before third-party sources detected it. This six-month lead gave us time to restructure our cost base and realign our investments.

We now use a short term approach to predict the state of the hardware, software and services market by country for the next six months. We’ve also closed the loop by feeding the projections from this into the long term tools to continuously refine our long-term forecasts and monitor downside risks as well as upside opportunities.

Of course, no forecasting tool will be 100% spot on all the time. But as companies battle test and tweak the array of tools at their disposal, they get closer all the time.

Do you know which industry is adopting analytics fastest? Do you know which industry has the biggest problem with social skills? Now you can find out.

The latest IBM Tech Trends Study surveyed over 1200 IT and business decision-makers – IT managers, business professionals and IT practitioners from 16 different industries and 13 countries – to assess how and why enterprises are adopting four emerging technologies – Mobile, Analytics, Cloud and Social Business – that are dramatically transforming how enterprises operate.

The study showed that Business Analytics and Mobile Computing represent a large swell, with over half of respondents already adopting these technologies. Cloud Computing and Social Business form a coming wave, with 40% currently piloting or planning to adopt by 2014. Furthermore, enterprises’ projected investment in the four areas is surging: 55% or more plan to increase investment in Mobile, Cloud, and Business Analytics, and 43% project increased investment in Social Business.

Despite the momentum in these areas, the study also uncovered a critical shortage of IT skills: Across all four technology areas, only about 1 in 10 companies reports having all the skills needed to be successful, and a quarter of respondents report major skill gaps.

A Deeper Dive: the Tech Trends Industry Dashboard

Today we’re launching a new interactive dashboard that allows you to explore the study findings in a dynamic way, by industry and by tech area. You can investigate adoption, investment, and skills for a particular industry within each tech area and sort to see how that industry compares to others, or to the cross-industry average.

Here’s one example. I chose Analytics, then Adoption Levels, and sorted by “Deployment” (and also clicked to “show percentages”):

We see that Insurance, Media and Entertainment, and Banking are the top three front runner industries in terms of high adoption of Analytics. Where does your industry fall?

You can also click on a particular industry name to bring up a graph specific to that industry; here we see Analytics skill levels reported by Media and Entertainment organizations:

It appears that Media and Entertainment is doing better on Analytics skills than the average: 23% have all the skills they need for Analytics, versus just 13% across all industries.

Sharing your insights

Are you successfully surfing one of the big tech waves but getting knocked down by another? Regarding your enterprise itself, do you think you’re outpacing your industry, keeping up, or lagging?

If you have particular insights about your industry’s position, please share them. All the graphs you encounter in the exploration are shareable – use the social media buttons or the embed code located beneath each graph to embed the graph within a blog, web page, or social media site. The embedded graph retains all the interactive functionality of the full dashboard.

Happy exploring – and we hope you’ll join the conversation around these findings!

If your enterprise is working with Big Data, or at least beginning to stick your toe in the water, and you're not thinking about the concept of "signal", you're about to make a big mistake. Identifying the signal is what will enable you to leverage Big Data effectively. And if you don't, you're going to spend a lot of time and money chasing red herrings.

When we rely on data for decision making, what qualifies as a signal and what is merely noise? In and of themselves, data are neither. Data are merely facts. When facts are useful, they serve as signals. When they aren’t useful, data clutter the environment with distracting noise.

For data to be useful, they must:

Address something that matters

Promote understanding

Provide an opportunity for action to achieve or maintain a desired state

When any of these qualities are missing, data remain noise.

I like this definition. It fits hand in hand with the concept of Marketing Science that we proposed earlier this year. Insights (aka signal) are only valuable in so far as they drive business outcomes. And if you're developing insights that influence action within your enterprise, you had better make sure that what you're looking at is actually signal.

This is where Big Data is presents challenges. In his post, Few makes the absolutely correct point that data are noisy. And when data increase dramatically in volume, velocity, and variety (aka it gets BIG), that noisiness grows right along with everything else. All of a sudden, it becomes that much harder to correctly identify signal. As Few points out:

Finding a needle in a haystack doesn’t get easier as you’re tossing more and more hay on the pile.

If you listen to some of the discussion around Big Data, you could easily walk away thinking that if you can capture it, all you need to do is run it through some sophisticated analytic software and "boom" you've got new insights.

The problem with this approach is that pesky noise. As you start dealing with huge data sets, it becomes relatively easy to find "statistically significant noise". You may think you're looking at signal, but instead you're just finding random patterns in the noise that happen to look like signal. This is what can happen when analysts are given lots of data and told to go find something.

How do you combat this? Part of it, as Few points out, is having data analysts that have a deep understanding of how to detect signal and the associated challenges that Big Data presents. The other part, is in how you approach data analysis in the first place.

Again, I'll reference our Marketing Science framework and propose that by applying a scientific approach to data collection and analysis, you improve your ability to correctly identify signal. Instead of randomly looking for patterns in the data, by developing hypotheses and then testing and refining them, you're able to focus on signal that (a is more likely to actually be signal and b) will help drive the business forward.

We've seen some really interesting and impactful results internally with the Marketing Science framework. We've developed insights that both drive business outcomes and challenge conventional thinking. I'll be highlighting a few of these examples in future blog posts. In the meantime, I'd love to get your feedback on what challenges you've experienced with identifying signal within Big Data.

The Guardian's Media Network recently hosted a live chat around the topic of how CMOs can align and use digital marketing and data analytics - two areas we've taken a close look at since the inception of the IBM Center for Applied Insights.

The Guardian notes:

Big data (and analyzing that data) means that marketing professionals are now getting even closer to the customer – they know more about audiences than ever before, with pinpoint precision. At their fingertips a marketer now has detailed facts and figures about consumer browsing habits, their favorite brands, how they use social media. It means that campaigns can be targeted, analyzed and proved better than ever.

It becomes the job of marketers and CMOs to make sense of all that data and not get lost in the noise. Doing this, takes an analytical and curious approach to data. It's easy to find the "big numbers" but more challenging to find the "right numbers." As Surjit Chana, CMO of IBM Europe, has said, the core principles of marketing haven't changed. What has changed, dramatically, is how those principles come to life in today's marketing campaigns, customer experiences, and business results. In our paper, Marketing Science: From descriptive to prescriptive, we found that only 23% of marketing professionals use tested analytic approaches to understand the vast amount of data they have access to. More traditional marketers, using data to describe outcomes but not determine actions, consistently use data at face value - without applying data models or scientific thinking.

When technology and analytic skills don't exist in the marketing teams, it makes perfect sense to build partnerships with those who do. The closest partner in most organizations is IT. Thus, the renewed focus on CMO + CIO collaboration. We're continuing to watch, collaborate, and recommend approaches to our C-Suite colleagues. Check out "Understanding leading retailers" to see how the retail industry is collaborating with IT and partners to serve customers better.

I came across this Smarter Commerce video a couple of weeks ago and I really like because it gets to the core of what Smarter Commerce is and why you should care about it. If you take a few minutes to watch it, what you’ll notice is that it keeps coming back to the customer as a central theme.

And at the end of the day, that’s really what Smarter Commerce is all about. It’s taking all of that data you’re collecting, helping you develop insights about your customer and the marketplace, and then applying all of that to every aspect of your business.

We recently conducted some research on a couple of specific areas with our colleagues from IBM DemandTec. Specifically, we looked at retail merchants and CPG sales organizations. The retail merchant research was conducted in collaboration with Planet Retail and the CPG sales org research was conducted in collaboration with Kantar Retail.

As we looked at the data from these research projects, we kept coming back to the same central theme as the video above: the customer. Everybody talks about being customer-centric. It’s almost a cliché. But what we found was that while everybody talks about it, most aren’t actually doing it.

Merchants tend to be product and category oriented, and CPG sales orgs tend to focus on their customer, the retailer, rather than the end consumer. Now we’re not saying that merchants should forget about product categories or that CPG sales teams should ignore the retailer, but we found that groups that placed a strong focus on the customer (or more specifically the consumer when talking about CPG) tended to outperform those that didn’t.

These “leaders” were customer-focused and collaborative, both within their enterprises and externally with vendors and partners. And they also made much greater use of analytics to uncover insights about their customers and the marketplace. The differences were really quite striking. For example, Leading Merchants were 1.6x more likely on average to use analytics to drive merchandising decisions, while Leading CPG Sales Organizations were more than 1.7x more likely to use analytics to improve product innovation.

In my previous post, I emphasized the importance of consumer focus for CPG companies. We, at the IBM Center for Applied Insights, have been working on a comprehensive global study* to gain more quantitative and qualitative insights about the increasing consumer focus of these CPG companies.

For the purpose of this study, we have segmented the market in terms of the degree of consumer focus and the use of analytics by the survey respondents. In this post, I would like to point out towards the most notable finding of the lot: existence of a “Leader” group amongst the survey respondents which enjoys much more clout with the retailers. In fact, they are nearly three times less concerned about needing a retailer’s approval to execute their plans, and 1.4 times less concerned about seeing their planning processes extended as a result of delays. They also exhibit superior financial performance over the rest. Between 2009 and 2012, the leading publicly quoted consumer products companies in our sample saw their stock prices rise 1.6 times faster than the rest (16 percent cumulative annual growth rate for Leaders compared to 10 percent cumulative annual growth rate for Others).

The companies in the Leader group use advanced analytics and collaborate extensively (both internally between functions and externally with retailers) to develop a high degree of consumer focus.

As shown in the figure 1 below, they comprise of about 15 percent of the total respondents.

The executive presentation will be delivered at the IBM Smarter Commerce event at Nashville this week.

For more details and insights on what exactly are these leaders doing differently than the rest and what steps can be taken to become one, revisit this space in a month. The Center and IBM DemandTec are authoring a complete paper on this topic, due out by the end of June.

I look forward to your comments and observations. Please click “Add a Comment” below or “More Actions” to share this with others.

Note - For the purpose of this study, we conducted telephone interviews with 356 senior sales executives at consumer products companies in Australia, Canada, India, the United Kingdom and United States, between February, 2013 to March, 2013. These respondents cover 10 product categories. Forty-six percent of them work for large enterprises (employing 1,000 or more people), while 54 percent work for medium-sized enterprises (employing 100-999 people).

We recently put together a nice video that provides an overview of Marketing Science. What is Marketing Science exactly? Well you can either watch the video or take a look at our Whitepaper. But the short version is that it's a way for marketers to deal with the challenges that "Big Data" presents by using a more rigorous scientifically grounded approach to develop insights and then using those insights to impact the business.

The concept itself really isn't very complicated. We've boiled it down to 3 steps: Architect Data, Apply Science, and Influence Action. However, the application of these concepts isn't always easy or straightforward. So over the coming months, I'll be posting about some of our own internal examples of applying Marketing Science to give you a better feel for what it looks like in practice.

And if your company has been engaging in Marketing Science, we'd love to hear about it. Who knows, maybe your example could be the subject of a future blog post.

The era of “Big Data” presents a variety of challenges and opportunities for marketers. With the increase in volume, velocity, and granularity of data, marketers can become much more precise in how they interact with both the marketplace and individual customers. But the same time, when you’re dealing with large volumes of data, it’s easy to over-fit your models and mistake “noise” for “signal”, to borrow a concept from Nate Silver’s excellent book, The Signal and the Noise.

This is something that we’ve been dealing with internally at IBM for a while now. In response, we’ve developed a framework internally that we think may help others refine their own approach to generating insights from data.

We call this framework “Marketing Science”. This is a 3-step framework consisting of “Architecting Data”, “Applying Science”, and “Influencing Action”. The fundamental idea is to apply the scientific method to developing insights within a business setting. This presents unique challenges in and of itself. But there are some basic concepts to keep in mind:

"Architecting" (or collecting and structuring) data is extremely important. The rest of the process depends on getting access to the right data from a variety of sources and if you haven’t done a good job of dealing with data across your enterprise, it’s like trying to run a 100m race with your shoes untied.

A hypothesis-test-refine approach to data analysis is central to the concept of Marketing Science. Developing and testing hypotheses is one of the main ways you limit your exposure to over-fitting data.

Within a business setting, insights are only valuable in so far as they’re able to inform decision-making and/or influence action. At the end of the day, driving business outcomes is the goal of Marketing Science. Keeping this in mind helps to keep you focused through the first two steps. And it means that once you’ve uncovered a nugget of insight, the real work may just be getting started as you take that insight back to the business.

Marketing Science is a fascinating topic that we’ll be talking about quite a bit more moving forward. We’ve conducted some market research that I think will be very enlightening and have started collecting some use-cases of how we’re applying these principles in a practical sense. In the meantime, if you have any comments or thoughts on developing insights from data, we’d love to hear from you.

I've previously written about our research of leading marketers, both their correlation with improved financial performance and what exactly they do differently than everybody else. We recently sat down with three leaders from our Enterprise Marketing Management team, Yuchun Lee, Elana Anderson, and Jay Henderson, and asked them to discuss our research and the implications of that research in more detail. Check out the video to get their take on why marketing matters, and how you can continue to engage with customers effectively and invest your marketing dollars intelligently.

Leave us a comment here or on YouTube to let us know if you're seeing similar trends in your enterprise.

There are four pivotal information technologies that are rapidly reshaping how enterprises operate: mobile technology, business analytics, cloud computing, and social business. All four of these technologies are potentially disruptive, and they also come with unique security concerns. Many people fear the security implications of employees bringing their own mobile devices to work, or storing mission critical databases in public cloud environments. Fear shouldn’t drive organizations away from these potentially transformative technologies. How are organizations overcoming their fears? How are they breaking though the “security wall”?

Recently IBM released the results of its 2012 Tech Trends Report, which looks at the adoption patterns of these four technologies. It is based on a survey of over 1,200 professionals who make technology decisions – the respondents came from 16 industries and 13 countries. As part of the analysis, three different types of organizations were identified:

Pacesetters (20%) believe emerging technologies are critical to their business success and are using them to enable new operating/business models. They’re also adopting ahead of their competition.

Followers (55%) agree that these technologies are important and can provide critical capabilities and differentiation, but they generally trail Pacesetters in adoption.

Dabblers (25%) are generally behind or, at best, on par with competitors in terms of adoption. They’re less strategic in their use of emerging technologies, namely citing greater efficiency or new capabilities in selected areas.

One common thread across all three of the identified groups is that security is a significant area of importance and concern. In fact, 62% of respondents cite security as one of the three most important areas facing their organization over the next two years, with 27% rating it number one. One interesting aspect is that, the less mature an organization is with respect to the four strategic technology areas, the more security rates as an area of importance and focus. Seventy-seven percent of the Dabblers cited security as a top-three area of importance, versus only 49% of the more mature Pacesetters. Why is that? Perhaps the Dabblers don’t fully understand, or trust, that there are security technologies, policies and practices that can ensure a more secure approach overall. Or perhaps they lack the experience the Pacesetters have.

“Security and privacy are not always treated as first-order problems. Things are deployed and made widely available without regard for security and privacy. In a best-case scenario, security and privacy are thought of as add-ons. Worst case, they’re ignored completely.”
– Dr. Eugene Spafford, Professor and Executive Director of the Center for Education and Research in Information Assurance and Security, Purdue University

Besides being an area of significant importance, security is also seen as a significant barrier to technology adoption by the survey respondents. Information security is ranked as one of the top two barriers to adoption across the four technology areas – more than integration, inadequate skills or regulation and compliance. Overall, security is the biggest barrier for a majority of respondents for mobile (61%) and cloud (56%) adoption. Security is cited less often as the top adoption barrier in social (47%) and analytics (31%). As shown by the dark blue bars in the graph below, there isn’t a huge gap between the groups (9-11%) when it comes to security concerns, but, in general, less mature Dabblers see security as more of a barrier than the more mature Pacesetters. The exception is analytics, which has the lowest adoption barrier. Perhaps Pacesetters better understand the potential risks in implementing advanced analytic systems.

Another part of the security wall blocking the full realization of the benefits of the four technologies is that organizations’ current IT security policies aren’t sufficient. The figure above generally shows correlations between viewing security as a barrier to adoption (dark blue bars) and inadequate security policies (light blue bars). The Pacesetters are more confident across the board, with a majority saying that their security policies are adequate. The “adequate policies gap” between the Pacesetters and Dabblers ranges from 13% to 32%, a fairly wide margin. This tells us that organizations that have the right security policies in place are more confident, and less likely to see security as a barrier. For the others, there is a gap between their fears and taking the steps needed to address those fears.

Another tool organizations are using to attack the security wall is skills development. A majority of the respondents know that security is an issue and are working hard to boost their confidence. Overall, 70% of organizations are planning to develop or acquire skills in “mobile security and privacy” and “cloud security” – the two technology areas where security is seen as the biggest barrier.

Security is tightly intertwined with the four technology areas discussed. You shouldn’t pursue cloud, mobile, social or analytics endeavors without also focusing on needed security technologies, skills, policies and practices. The more you focus on policies and skills, the less likely you will see security as an impediment. Treat security as a business imperative and make it a priority. Design security in from the start of any project. Doing this will increase confidence and help to tear down the walls that are slowing the adoption of important, transformative technologies.

As a former marketer myself, I know that marketing is often marginalized within enterprises, particularly those with strong scientific or development organizations. Marketing is often viewed as being responsible for the “soft stuff” that looks pretty but doesn’t have any real impact on the business. I’m here to tell you that this view is wrong, and if you don’t realize it quickly, your competitors will.

We recently surveyed 362 marketers from around the world, across more than 15 industries, and found that Leading Marketers’ enterprises had 40% greater Revenue growth and twice the Gross Profit growth over the past 3 years when compared to the rest.

What exactly is a Leading Marketer?I’m glad you asked. We identified 2 essential traits of effective marketers: “Effective Engagement” and “Intelligent Investment”. Essentially we defined Leading Marketers as those who had a high level of responsibility forengagingwith customers across channels as well as a sophisticated approach toinvestingmarketing resources.

We then looked at publicly available financial data and found that when we correlated that to our segmentation of leading marketers, a clear trend emerged: Leading Marketers’ enterprises performed better financially.

So how, exactly, do you develop a Leading Marketing organization within your enterprise? Like most things in today’s world the answer is complex but grounded in the principles of Marketing 101. It can be as simple as the 4P’s or as complicated as developing a collaborative relationship with other functional areas within the enterprise. I’ll be blogging more about this topic and other insights from our study over the coming weeks, but get a sneak preview by reading our executive report, How Leading Marketers Outperform: Effective Engagement and Intelligent Investment.

If there is a particular topic you’d like me to talk about, please login and leave me a comment, below.

As you can see, Healthcare has a distribution of 31% Outperformers and 69% Others. Overall, that breakout is similar to many other industries – with one exception. 19% of respondents identified that they had a high Anticipate capability with a low Listen capability.

This is unusual as typically most organizations will develop strong Listen capabilities before investing in Anticipate capabilities. The majority of healthcare organizations we surveyed followed this more typical model, but the higher number of outliers here suggests a couple of things: 1) Healthcare firms recognize the benefits of applying analytics to data in order to develop insights and 2) they could be dealing with an overwhelming amount of patient data that limits their ability to listen effectively.

Most Important Issues over the next 3 years:

We also saw something a bit different when we asked healthcare organizations about their most important issues over the next 3 years. Most other industries are focused on technical, economic or organizational challenges. Healthcare firms are clearly most focused on patient safety. It was selected as a top issue by 55% of respondents, with the next highest issue, compliance, only being selected by 37% of respondents. We also saw that an often talked about topic, cost control landed in 5th place with 28% of respondents selecting it.

Digging a little bit deeper into the data, we found that the vast majority of healthcare Outperformers collected data at every customer interaction (82%) and were 1.7x more likely to do so than the Others. This was the 2nd highest overall percentage behind retailers.

However, when we asked about their ability to capture unstructured data, we saw that healthcare organizations are struggling. Only 45% of the Outperformers captured unstructured data (2nd lowest overall) compared with 30% of the Others. This lends at least some credence to the theory mentioned above that some healthcare organizations may be struggling to keep up with the volume of data that is now available to them.

Also supporting the theory that healthcare organizations are embracing the value of analytics, when we asked who they shared insights with, we saw some of the highest numbers of any industry.

82% of Outperformers (vs 44% of Others) use insights to guide the actions of executive decision makers. 87% of Outperformers (vs 33% of Others) share insights with suppliers and business partners. 87% of Outperformers (vs 54% of Others) used analytics to recommend actions to patients. The Outperformer numbers were some of the highest of any industry and are all very logical ways for healthcare organizations to leverage insights from analytics.

This same theme continues when we look at where healthcare organizations realize value from analytics. 60% of Outperformers (vs 42% of Others) realize value when it comes to Patient Relationship Management. 48% of Outperformers (vs 33% of Others) realize value from Workforce Planning and Optimization. Again, these were all large percentages compared to other industries.

We did however see that there was a gap when it came to collaborating and sharing knowledge. Only 18% of Outperformers were realizing value here. That said, the overall numbers across industries were low for collaboration and sharing, but with analytics providing such strong value in a number of areas for healthcare organizations it seems logical that a possible next step would be to build better collaboration and sharing capabilities. After all, if nobody knows about an insight that’s been developed regarding a patient, drug, procedure, etc, it can’t add significant value.

Overall the data we see from healthcare organizations suggests that Outperformers are truly leveraging their Anticipate capabilities to drive value for the organization and for patients. That said, there’s still opportunity to add value by continuing to develop the Listening capability while making sure that insights and knowledge can be shared across the organization.

Ever wonder what makes one infographic hit the mark and another one miss? There's more science to it than you might think.

Information graphics – visual representations of information, data, knowledge, or concepts – have been around for millennia, and humans have long mapped data in order to organize what they see, filter out extraneous details, reveal patterns, suggest further exploration, and ultimately better understand the world around them.

"Why should we be interested in visualization? Because the human visual system is a pattern seeker of enormous power and subtlety. The eye and the visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers. At higher levels of processing, perception and cognition are closely interrelated, which is the reason why the words ‘understanding’ and ‘seeing’ are synonymous.”
(Colin Ware, Information Visualization: Perception for Design, Academic Press, 2000)

Anyone responsible for creating infographics in order to communicate complex information effectively can benefit by taking advantage of lessons from visual perception research.

Prof. Colin Ware, of the Data Visualization Research Lab at the University of New Hampshire, explains:

“… the visual system has its own rules. We can easily see patterns presented in certain ways, but if they are presented in other ways, they become invisible. … The more general point is that when data is presented in certain ways, the patterns can be readily perceived. If we can understand how perception works, our knowledge can be translated into rules for displaying information. Following perception-based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey the rules, our data will be incomprehensible or misleading.”

One important lesson we can leverage from vision science is an understanding of which elements will prominently “pop out” of an image – thanks to a mechanism known as “pre-attentive processing.” As our brains start to process an image, massively parallel processes detect image elements that are differentiated by low-level characteristics such as form, color, motion, and spatial position. The principles of pre-attentive processing govern which visual elements grab our attention first, before we’ve even begun to consciously process the image.

Here’s a simple example to illustrate the point. Count the number of 9’s appearing in this set of digits:

This time was a lot easier and quicker, thanks to the fact that our brains process lightness pre-attentively.

Some features that are pre-attentively processed include: color (hue and intensity), form (line orientation, line length and width, size, shape, curvature), motion (flicker, direction), and spatial position (2D position, spatial grouping).

For some more pre-attentive fun, visit the demo at this site, choose a feature, and see how immediately and easily your visual system is able to process it.

Understanding what kinds of features are pre-attentively processed has important implications for visual displays. When designing for critical situations such as air traffic control, flight display, or clinical care dashboards, it’s crucial to understand how to make certain symbols or elements stand out from others so they can be interpreted and acted upon immediately.

Likewise, if you’re designing infographics, it’s also important to understand which elements will be seen at first glance – they’re your first chance to grab your reader’s attention, even before conscious processing. Using color, size, shape, orientation, and other pre-attentive attributes, you’ll need to carefully craft which are the most important elements that should “pop out” first.

But choose carefully; not every element of your infographic can stand out. Vision science tells us that pre-attentive elements become less distinct as the assortment of patterns increases. Imagine a bumblebee swarming among flies; the bee is easy to pick out. Now imagine wasps, hornets, and yellowjackets joining the swarm, and the bumblebee will get lost in the mix. So it is with an infographic: As the multitude of competing pre-attentive elements increases, their “power to pop” will be diminished.

If you look at the distribution matrix below, the first think you’ll notice is that 46% of the respondents
were identified as “Outperformers”.This
was the highest ratio of Outperformers of any of industry we
surveyed.Simultaneously, 33% of
respondents were identified as having low Listen and Anticipate capabilities.

What we’re seeing here is an interesting dichotomy.Simultaneously, a significant proportion of
the industry are Outperformers while a smaller yet significant proportion of
the industry has low Listen and Anticipate capabilities – without much in between. This tells us that while Banking is clearly one of the more advanced industries when it comes to data and analytics, there are still significant opportunities for improvement.

As you might expect, the Banking industry Outperformers capture quite a
bit of data.79% captured customer data at every interaction (2.1x more than the Others).Additionally 58% of the Outperformers
captured unstructured data (1.6x more than the Others).

What is that data used for?Interestingly, both Outperformers and Others
used analytics to guide the actions executive decision makers (83% and 79%
respectively).This was by far the
smallest gap in this capability between the Outperformers and Others of any
industry and suggests that this capability is “table stakes”.

However, there are several uses of data that differentiate
Outperformers from Others.First, 84% of
Outperformers provide insights to suppliers and business partners (2.4x more
than the Others).Second, the Banking
Outperformers tied for the highest percentage usage of analytics
to recommend actions to customers among the industries (87% - 1.7x more than the Others).

Finally, we saw 2 very interesting results when we asked
where Banks realized value from analytics.We found that 37% of Outperformers realized value when they used
analytics to drive workforce planning and management.This was particularly interesting because the
Outperformers were 9(!) times more likely to realize value here than the
Others.

The other interesting result was one that we haven’t found
a complete explanation for (yet!).65% of the Others vs 48% of the Outperformers realized value from analytics in regards to
risk management.This was a
counter-intuitive result, so there’s clearly something interesting going on
here.

My current theory is that this result doesn’t mean that
these Outperformers aren’t engaged in risk management activities.To the contrary, it likely means that about half of them have
other systems in place that drive their risk management activities without relying significantly on Analytics.They
may make more use of policies, procedures, limits, and executive
oversight. Or perhaps their greater use of analytics to
engage with customers, suppliers, and business partners is effectively
providing indirect risk management.

Hopefully this has provided you with some interesting
insights into the Banking industry.As
always, please feel free to leave a comment or
send me an e-mail if you have
any questions.I’d be particularly
interested in any thoughts you might have on risk management in the Banking
industry.

Welcome! This is the first post in a series of articles I’ll be writing over the coming months that delve a bit deeper into some of the more interesting findings from our State of Smart research here at the IBM Center for Applied Insights. Today we’ll be discussing some of the interesting data points for the retail industry including some surprising findings about CRM.

If you’re not familiar with our State of Smart work, an overview of our research and findings can be found in our Executive Report. We surveyed over 1100 executives worldwide across 9 industries to determine their organizations’ information and analytics capabilities (we refer to these capabilities as “listen” and “anticipate”). We found that organizations with these capabilities significantly outperformed their peers: 1.6x revenue growth, 2x EBITDA growth, and 2.5x stock price appreciation over a five year period. Not bad, huh?

But this only tells part of the story. We also asked these enterprises about where they realize value from analytics and how they deploy “listen” and “anticipate” capabilities. So let’s dig into the retail data a bit deeper.

For our purposes, we’re going to refer to retailers in the top right quadrant as “Outperformers” and everybody else as “Others”. Only 29% of retailers are Outperformers. About 62% of retailers have a high level of “listen” capabilities while only 38% of retailers have a high level of “anticipate” capabilities.

What this tells us is that retailers are pretty good at “Listening” – i.e. capturing data. By and large, most retailers have done a good job of laying down an information foundation. However, a much smaller proportion of those retailers are then translating that data into actionable insights.

So we know that retailers have room to improve when it comes to leveraging the data that they capture. What other interesting insights did we uncover?

For starters, as you might expect from the high “listen” capabilities, retail Outperformers are very good at capturing different types of data. Specifically, 84% of Outperformers capture data at every customer interaction (this was the highest % across any industry we surveyed). The 'data at every customer interaction' spread between outperformers and others is 2.2x, the second highest gap among the nine industry categories.

Additionally, the 56% of the Outperformers captured unstructured data versus 35% of the Others. Essentially the Outperformers are looking beyond individual transaction data and are mining social media, weather patterns, etc to drive more robust information for applied decision making.

The Outperformers then leverage this information to drive actionable insights about their customers. For example, 84% of Outperformers (vs 38% of Others) use their information and analytics capabilities to recommend actions to customers. This can take the form of both customer facing recommendations, such as cross-selling or up-selling opportunities, or internal actions such as identifying next best actions to convert abandoned baskets or reactivate a dormant customer.

The holy grail of retailers has long been to develop deep insights about customers from a variety of data sources and then use these insights to drive actions that positively impact the customer experience and consequently improve their top and bottom line. Our data shows that the Outperformers are doing just that.

What we’ve talked about so far is fairly intuitive. However in the course of analyzing the State of Smart data we saw several things that intrigued us. For instance, found that only 36% of Outperformers vs 31% of Others realized value from customer relationship management. We expected the overall percentages to be higher and gap to be wider. The data suggest several things. First, the true value of CRM has likely not yet been realized by most retailers. Second, the outperformers haven’t yet found a way to drive the additional insights they’ve been generating into their CRM practices.

Hopefully you found this deep dive into our State of Smart retail industry data to be interesting and useful. If you're interested in calculating the potential impact that developing your "listen" and "anticipate" capabilities can have on your business, I suggest you take a quick look at our Smarter Merchandising and Smarter Shopping Experience toolsets. We've developed online calculators that let you quickly and easily get an idea of the potential economic benefits that leveraging analytics can have for your organization.

I’ll be posting more deep dive articles over the next few months. Check back next next week for a deep dive into the banking industry data. If you have any questions about this article or requests for future articles, please feel free to let me know.